Online or onsite, instructor-led live GraphX training courses demonstrate through hands-on practice how to implement GraphX to carry out graph computing across many machines in parallel.
GraphX training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live GraphX trainings in Bordeaux can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Bordeaux, Mériadeck
Nomade Bordeaux, 39 Rue du Château d'Eau, Bordeaux, france, 33000
Located in the heart of Bordeaux, in the Mériadeck quarter, fully equipped and air-conditioned, NOMADE allows mobile prof...
Located in the heart of Bordeaux, in the Mériadeck quarter, fully equipped and air-conditioned, NOMADE allows mobile professionals to settle down to work and share with other coworkers in a friendly atmosphere.
It offers 23 workstations in open space, a shared meeting room and 5 meeting rooms for rent in Bordeaux center.
The entire space can also be rented in the evening for your events.
Access: TRAM A MERIADECK station, TRAM B GAMBETTA station
In this instructor-led, live training in Bordeaux, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.
By the end of this training, participants will be able to:
Understand how graph data is persisted and traversed.
Select the best framework for a given task (from graph databases to batch processing frameworks.)
Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.
View real-world big data problems in terms of graphs, processes and traversals.
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